Yale Song, Zhen Wen, et al.
IJCAI 2013
Let X be a data matrix of rank ρ, representing n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1- norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum enclosing ball in the feature space are preserved to within ε-relative error, ensuring comparable generalization as in the original space. We present extensive experiments with real and synthetic data to support our theory.
Yale Song, Zhen Wen, et al.
IJCAI 2013
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guojing Cong, David A. Bader
Journal of Parallel and Distributed Computing
Ran Iwamoto, Kyoko Ohara
ICLC 2023